Generative AI’s true value lies in digital twins and trusted data

Energy Transition Talks

In part two of our Energy Transition Talks conversation on generative artificial intelligence (AI), CGI experts Diane Gutiw and Peter Warren further explore the implications and applications of AI in the energy and utilities industry. Building upon their discussion in part one, they examine how digital twins, change management and trusted data are shaping the use and performance of AI in energy organizations, ultimately looking to the future of AI as multimodal, human-driven technology solution.

The key to realizing AI value: integrated solutions and digital twins

Increasingly, the greatest benefits of generative AI are emerging not in single solutions, but in integrated, multi-model, multimodal ways of pulling in information, producing expert advice and automating certain functions.

The energy industry, says Diane, is “a great example of a very complex environment with lots of different types of media and data that can be leveraged by these new and upcoming technologies.”

In her view, AI is headed toward digital twin models and integrated solutions. In the energy industry, this increased data-driven automation can help make both the grid and operations more efficient.

Peter Warren shares one key use case for digital twins is to help organizations understand other markets better, as they transition their current model. “You might know your existing industry well,” he says, “but as you move from traditional carbon-based energy to something less carbon-based, be it hydrogen or electricity, you may not know those markets; being able to create a digital twin of something you haven’t formally understood is a huge benefit.”

Diane agrees and suggests that the adoption of a digital twin to represent an organization’s current environment is a great use case, especially where there’s a data-intensive end-to-end workflow. Not only does this provide a robust view of the existing environment, she says, “but also it allows organizations to look at different scenarios and leverage AI to say, for example, ‘What would happen to the grid if this event happened, and how could I automatically adjust?’”

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